{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    }
   ],
   "source": [
    "print(__doc__)\n",
    "\n",
    "from scipy import linalg\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from matplotlib import colors\n",
    "\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Colormap\n",
    "cmap = colors.LinearSegmentedColormap(\n",
    "    'red_blue_classes',\n",
    "    {'red': [(0, 1, 1), (1, 0.7, 0.7)],\n",
    "     'green': [(0, 0.7, 0.7), (1, 0.7, 0.7)],\n",
    "     'blue': [(0, 0.7, 0.7), (1, 1, 1)]})\n",
    "plt.cm.register_cmap(cmap=cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Generate datasets\n",
    "def dataset_fixed_cov():\n",
    "    '''Generate 2 Gaussians samples with the same covariance matrix'''\n",
    "    n, dim = 300, 2\n",
    "    np.random.seed(0)\n",
    "    C = np.array([[0., -0.23], [0.83, .23]])\n",
    "    X = np.r_[np.dot(np.random.randn(n, dim), C),\n",
    "              np.dot(np.random.randn(n, dim), C) + np.array([1, 1])]\n",
    "    y = np.hstack((np.zeros(n), np.ones(n)))\n",
    "    return X, y\n",
    "\n",
    "\n",
    "def dataset_cov():\n",
    "    '''Generate 2 Gaussians samples with different covariance matrices'''\n",
    "    n, dim = 300, 2\n",
    "    np.random.seed(0)\n",
    "    C = np.array([[0., -1.], [2.5, .7]]) * 2.\n",
    "    X = np.r_[np.dot(np.random.randn(n, dim), C),\n",
    "              np.dot(np.random.randn(n, dim), C.T) + np.array([1, 4])]\n",
    "    y = np.hstack((np.zeros(n), np.ones(n)))\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #############################################################################\n",
    "# Plot functions\n",
    "def plot_data(lda, X, y, y_pred, fig_index):\n",
    "    splot = plt.subplot(2, 2, fig_index)\n",
    "    if fig_index == 1:\n",
    "        plt.title('Linear Discriminant Analysis')\n",
    "        plt.ylabel('Data with\\n fixed covariance')\n",
    "    elif fig_index == 2:\n",
    "        plt.title('Quadratic Discriminant Analysis')\n",
    "    elif fig_index == 3:\n",
    "        plt.ylabel('Data with\\n varying covariances')\n",
    "\n",
    "    tp = (y == y_pred)  # True Positive\n",
    "    tp0, tp1 = tp[y == 0], tp[y == 1]\n",
    "    X0, X1 = X[y == 0], X[y == 1]\n",
    "    X0_tp, X0_fp = X0[tp0], X0[~tp0]\n",
    "    X1_tp, X1_fp = X1[tp1], X1[~tp1]\n",
    "\n",
    "    # class 0: dots\n",
    "    plt.scatter(X0_tp[:, 0], X0_tp[:, 1], marker='.', color='red')\n",
    "    plt.scatter(X0_fp[:, 0], X0_fp[:, 1], marker='x',\n",
    "                s=20, color='#990000')  # dark red\n",
    "\n",
    "    # class 1: dots\n",
    "    plt.scatter(X1_tp[:, 0], X1_tp[:, 1], marker='.', color='blue')\n",
    "    plt.scatter(X1_fp[:, 0], X1_fp[:, 1], marker='x',\n",
    "                s=20, color='#000099')  # dark blue\n",
    "\n",
    "    # class 0 and 1 : areas\n",
    "    nx, ny = 200, 100\n",
    "    x_min, x_max = plt.xlim()\n",
    "    y_min, y_max = plt.ylim()\n",
    "    xx, yy = np.meshgrid(np.linspace(x_min, x_max, nx),\n",
    "                         np.linspace(y_min, y_max, ny))\n",
    "    Z = lda.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z[:, 1].reshape(xx.shape)\n",
    "    plt.pcolormesh(xx, yy, Z, cmap='red_blue_classes',\n",
    "                   norm=colors.Normalize(0., 1.), zorder=0)\n",
    "    plt.contour(xx, yy, Z, [0.5], linewidths=2., colors='white')\n",
    "\n",
    "    # means\n",
    "    plt.plot(lda.means_[0][0], lda.means_[0][1],\n",
    "             '*', color='yellow', markersize=15, markeredgecolor='grey')\n",
    "    plt.plot(lda.means_[1][0], lda.means_[1][1],\n",
    "             '*', color='yellow', markersize=15, markeredgecolor='grey')\n",
    "\n",
    "    return splot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Linear Discriminant Analysis vs Quadratic Discriminant Analysis')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_ellipse(splot, mean, cov, color):\n",
    "    v, w = linalg.eigh(cov)\n",
    "    u = w[0] / linalg.norm(w[0])\n",
    "    angle = np.arctan(u[1] / u[0])\n",
    "    angle = 180 * angle / np.pi  # convert to degrees\n",
    "    # filled Gaussian at 2 standard deviation\n",
    "    ell = mpl.patches.Ellipse(mean, 2 * v[0] ** 0.5, 2 * v[1] ** 0.5,\n",
    "                              180 + angle, facecolor=color,\n",
    "                              edgecolor='black', linewidth=2)\n",
    "    ell.set_clip_box(splot.bbox)\n",
    "    ell.set_alpha(0.2)\n",
    "    splot.add_artist(ell)\n",
    "    splot.set_xticks(())\n",
    "    splot.set_yticks(())\n",
    "\n",
    "\n",
    "def plot_lda_cov(lda, splot):\n",
    "    plot_ellipse(splot, lda.means_[0], lda.covariance_, 'red')\n",
    "    plot_ellipse(splot, lda.means_[1], lda.covariance_, 'blue')\n",
    "\n",
    "\n",
    "def plot_qda_cov(qda, splot):\n",
    "    plot_ellipse(splot, qda.means_[0], qda.covariance_[0], 'red')\n",
    "    plot_ellipse(splot, qda.means_[1], qda.covariance_[1], 'blue')\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 8), facecolor='white')\n",
    "plt.suptitle('Linear Discriminant Analysis vs Quadratic Discriminant Analysis',\n",
    "             y=0.98, fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, (X, y) in enumerate([dataset_fixed_cov(), dataset_cov()]):\n",
    "    # Linear Discriminant Analysis\n",
    "    lda = LinearDiscriminantAnalysis(solver=\"svd\", store_covariance=True)\n",
    "    y_pred = lda.fit(X, y).predict(X)\n",
    "    splot = plot_data(lda, X, y, y_pred, fig_index=2 * i + 1)\n",
    "    plot_lda_cov(lda, splot)\n",
    "    plt.axis('tight')\n",
    "\n",
    "    # Quadratic Discriminant Analysis\n",
    "    qda = QuadraticDiscriminantAnalysis(store_covariance=True)\n",
    "    y_pred = qda.fit(X, y).predict(X)\n",
    "    splot = plot_data(qda, X, y, y_pred, fig_index=2 * i + 2)\n",
    "    plot_qda_cov(qda, splot)\n",
    "    plt.axis('tight')\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=0.92)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
